where \(\mathbf {Q}\) , \(\mathbf {K}\) , and \(\mathbf {V}\)  are linearly transformed from the input \(\mathbf {X}\) , and \(H\)  is the number of heads in multi-head self-attention [1]}. The similarity is related to that both operators collect pixels in the neighborhood \(\delta \)  or a less bounded range \(\Omega \)  through a weighted sum. On one hand, the computation of involution can be viewed as a spatial attentive aggregation. On the other hand, the attention map (also called affinity or similarity matrix) \(\mathbf {QK}^\top \)  in the self-attention mechanism can be considered as a kind of involution kernel \(\mathbf {\mathcal {H}}\) .
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